Stakeholders’ Consensus to Guide the Minimum Impairment Criteria in Wheelchair Basketball
Bibliographic record
Abstract
The International Paralympic Committee athlete classification code mandates sports to have defined minimum impairment criteria, describing the minimum level of an eligible impairment an athlete must possess, to be able to participate in that sport. The aim of this study was to establish stakeholders' consensus for the minimum impairment criteria in wheelchair basketball. From a pool of 48 expert stakeholders (identified via an international medical and scientific working group), 39 completed a 4-round Delphi survey. Questions were answered on the method of assessing each eligible impairment, and the level of impairment that should constitute the minimum impairment criteria. This study indicated where stakeholder consensus existed and noted that consensus was developed for impaired muscle power, impaired passive range of motion, leg length difference, hypertonia and ataxia. No consensus was found for limb deficiency and athetosis. Participants raised concerns with using subjective measurement scales for assessing certain impairments, whilst also calling for more quantitative research to be conducted into the level of impairment that should constitute the minimum impairment criteria. For these research findings to form practical minimum impairment criteria that are part of a wheelchair basketball classification system, it is required to examine their feasibility by conducting further research.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.233 | 0.244 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.005 | 0.016 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".